We aim to perform procedure learning in a self-supervised fashion, unlike previous works [1]}, [2]}, [3]}, which assume the availability of mapping between video frames and key-steps.
Also, different from weakly supervised approaches [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, we neither use the number of key-steps required to perform the task nor an ordered or unordered list, as it requires viewing the videos or defining heuristics, leading to scalability issues [13]}, [14]}.
Additionally, learning various procedures requires numerous videos and annotating all the videos would consume considerable resources.
Motivated by this, we create CnC as a self-supervised framework for procedure learning to create a scalable and efficient solution.
